There is a huge incentive for gene expression analysis and identification of biologically meaningful clusters from microarray data. However, the high dimensionality of the data poses challenges for this task. Here, to reduce this problem of irrelevant dimensions, we consider three different projection methods, viz. principal components analysis (PCA), correspondence analysis (CA), and multiple discriminant analysis (DA). To account for the possibility of pleiotropy, where the expression of certain genes may be related to more than one phenotypical condition, we use fuzzy clustering on the lower dimensional space generated by PCA, CA, and DA. Fuzzy clustering permits partial belonging of an attribute, such as gene expression, to different functionalities and hence is eminently suited for this task. To determine the optimum number of clusters, we evaluate various cluster validity indices. In this paper, we compare these methodologies when applied to the data generated by a genetic network simulator (eXPatGen) and also to the experimental micro array data available for yeast S. cerevisiae. (C) 2006 .